Research Questions in Quantitative Political Science
PSCI 3300.003 Political Science Research Methods
A. Jordan Nafa
University of North Texas
1/26/23
How Should we Study Politics?
Political science is a field characterized by a diverse range of approaches to inquiry and debates about how we ought to study political phenomena have long animated the discipline.
Normative
Focused on subjective, moral questions about the world and how things ought to be.
Empirical
Focused on objective explanation and description, questions about how the world is and why.
Normative approaches to the study of politics date back thousands of years and feature prominently in the sub-field of political philosophy.
How should the world look? Asks for a moral judgement
Who should be responsible for paying for the consequences of climate change?
Should we fire Elon Musk into the Sun?
Should women have autonomy over their reproductive choices?
Is it fair to forgive student loan debt?
Normative arguments are common in certain areas of law and philosophy but have no place in this course as they do not lend themselves to scientific answers
Empirical Approaches
Empirical approaches are those that aim to apply the scientific method to the study of politics and hold a dominant place contemporary political science.
Empirical approaches can be descriptive or causal, quantitative or qualitative, experimental or observational but they all aim to answer some question about how, what, or why the world is.
Description focuses on observing and measuring the state of the world; it aims to answer questions about who or what in relation to some phenomena (Gerring 2012).
What is democracy and how can we operationalize it?
Who won the 2020 presidential election election?
2020 Presidential Election Vote Totals
Evolution of Liberal Democracy in America
Empirical Approaches
Description has a valuable place in political science and accurate description is essential to empirical research.
Descriptive approaches tend to lend themselves to dichotomous answers
Is country \(A\) more democratic than country \(B\)?
Is American democracy in decline?
Did Donald Trump lose the 2020 U.S. presidential election?
Yet, it is necessarily inferior to causal approaches because it cannot answer questions of why or how things happen
We’ll take a more detailed look at the role of description in quantitative political research when we discuss measurement
Empirical Approaches
Causal approaches are concerned with explaining why some phenomenon occurs in the world (Samii 2016).
Contemporary political science is a discipline interested in answering causal questions.
Why do poor conservatives tend to vote against their own economic interests?
How do gender-inclusive peace processes influence the risk of conflict recurrence?
How would the world change if we fired Elon Musk into the Sun?
Our focus in this class will be primarily on causal questions and entirely on empirical approaches to the study of politics
What is Causal Inference?
Does forgiving student loan debt increase inflation?
Imagine student loan debt is forgiven and inflation increases
Would this increase have happened if student loan debt had not been forgiven?
How do gender-inclusive peace processes influence the risk of conflict recurrence?
Conflicts that terminate with gender-inclusive peace provisions tend to be less likely to recurr
Would conflict have recurred in the abscence of these gender-inclusive peace provisions?
Causal inference is about counterfactuals
What is Causal Inference?
A counterfactual is what would have happened in the absence of some intervention.
Imagine a study of \(\color{sample} n\) individuals
\(\color{sample} n_{\color{treat} 1}\) are assigned some treatment
\(\color{sample} n_{\color{untreat} 0}\) do not receive the treatment
For each individual \(\color{index} i \color{operator}\in \{1, 2, \dots, \color{sample} n \color{operator}\}\) we observe the outcome \(\color{resp}Y_{\color{index}i}\)
Treatment status for each individual \(\color{index} i\)\[\color{treatment} X_{\color{index}i} \color{operator} = \begin{cases}\color{treat} 1 \text{ if treated}\\ \color{untreat} 0 \text{ if not treated}\end{cases}\]
Some set of pre-treatment covariates \(\color{confounders} Z_{\color{index}i}\)
What is Causal Inference?
Counterfactuals are questions about the data we do not observe, not the data we do.
We want to know the causal effect of \(\color{treatment} X_{\color{index} i}\) on \(\color{resp} Y_{\color{index} i}\)
If an individual is treated, \(\color{treatment} X_{\color{index} i} \color{operator} = \color{treat} 1\) and we observe some value of \(\color{resp} Y_{\color{index} i}\)
What value of \(\color{resp} Y_{\color{index} i}\) would we have observed if \(\color{treatment} X_{\color{index} i} \color{operator} = \color{untreat} 0\) instead?
Fundamental Problem of Causal Inference
For each individual \(\color{index} i\) we can only observe \(\color{treatment}X_{\color{index} i} \color{operator} = \color{treat}1\) or \(\color{treatment}X_{\color{index} i} \color{operator} = \color{untreat} 0\)
Causal inference is a missing data problem
How do we overcome this problem?
We make assumptions to bridge these parallel worlds
Firing Billionaires into the Sun
Imagine we are interested in whether firing billionaires into the Sun might cause some meaningful improvement in the world.
A study of \(\color{sample} n\) billionaires
For each billionaire \(\color{index} i \color{operator}\in \{1, 2, \dots, \color{sample} n \color{operator}\}\) we observe the state of the world \(\color{resp}Y_{\color{index}i}\) before and after they are assigned to either the treatment or control group
Treatment status for each billionaire \(\color{index} i\)\[\color{treatment} X_{\color{index}i} \color{operator} = \begin{cases}\color{treat} 1 \text{ if fired into the Sun}\\ \color{untreat} 0 \text{ if not fired into the Sun}\end{cases}\]
Some set of pre-treatment covariates \(\color{confounders} Z_{\color{index}i}\)
Firing Billionaires into the Sun
The causal effect of firing a billionaire into the Sun is
\(\color{resp}Y_{\color{index}i}\color{treat}(\text{Fired into the Sun})\color{operator} - \color{resp}Y_{\color{index}i}\color{untreat}(\text{Not fired into the Sun})\)
For each billionaire \(\color{index} i\) we can either fire them into the Sun or not fire them into the Sun, but it is impossible do both
This is the fundamental problem of causal inference
Also illustrates some practical limitations
No IRB would ever approve this study and billionaires are a hard to sample population 😭
Helps you keep things organized in appropriate subfolders
All file paths are relative to the .Rproj file’s location
Write code in scripts
Helps you keep track of and structure your data, analysis, etc.
Comment your code
Use seperate scripts for each part of your analysis, problem sets, etc.
We’ll talk more about Quarto and dynamically reproducible documents next week
Scripts in R
It is generally considered good practice to load the packages you use in a script and set any global options for the R session at the top of the script.
#---------------------R for Political Research: Lesson I-----------------------#-Author: A. Jordan Nafa-----------------------------Created: August 19, 2022-##-R Version: 4.2.1-----------------------------------Revised: January 26, 2023-## Set Session Options, you could also declare these in a .Rprofileoptions(digits =4, # Significant figures outputscipen =999, # Disable scientific notationrepos =getOption("repos")["CRAN"] # repo to install packages from)# Load Required Libraries, run install.packages("pacman") firstpacman::p_load("tidyverse", # Suite of packages for tidy data management "data.table", # Package for high-performance data management "dtplyr", # Package to interface between dplyr and data.tableinstall =FALSE# Set this to TRUE to install missing packages)
Since code is executed sequentially, this ensures any dependencies required for later code chunks have already been loaded prior to their execution.
Basic Calculations in R
In the simplest illustration, we can use R for both basic calculations like addition, subtraction, multiplication, and division
# Using R for Additionprint(2+2)# We can also find the sum of a sequence of numberssum(2, 4, 6)
Basic Calculations in R
In the simplest illustration, we can use R for both basic calculations like addition, subtraction, multiplication, and division
# Using R for Additionprint(2+2)# We can also find the sum of a sequence of numberssum(2, 4, 6)# Using R for Subtractionprint(6-2)
Basic Calculations in R
In the simplest illustration, we can use R for both basic calculations like addition, subtraction, multiplication, and division
# Using R for Additionprint(2+2)# We can also find the sum of a sequence of numberssum(2, 4, 6)# Using R for Subtractionprint(6-2)# Using R for Multiplicationprint(9*12)# We can also find the product of a sequence of numbersprod(9, 12, 36)
Basic Calculations in R
In the simplest illustration, we can use R for both basic calculations like addition, subtraction, multiplication, and division
# Using R for Additionprint(2+2)# We can also find the sum of a sequence of numberssum(2, 4, 6)# Using R for Subtractionprint(6-2)# Using R for Multiplicationprint(9*12)# We can also find the product of a sequence of numbersprod(9, 12, 36)# Using R for Divisionprint(4/2)
All R, HTML, and CSS code is provided for public use under a BSD 3-Clause License.
The files and code necessary to reproduce the content of this course are or will be made available via the course’s github repository with the exception of those covered by existing commercial copyright restrictions (i.e., copies of the assigned readings for the course).
References
Gerring, John. 2012. “Mere Description.”British Journal of Political Science 42(4): 721–46.